PwC
Website:
pwc.com
Job details:
At PwC, our people in risk and compliance focus on maintaining regulatory compliance and managing risks for clients, providing advice, and solutions. They help organisations navigate complex regulatory landscapes and enhance their internal controls to mitigate risks effectively. In actuarial services at PwC, you will be responsible for analysing and managing financial risks for clients through statistical modelling and data analysis. Your work will generate valuable insights and recommendations to help businesses make informed decisions and mitigate potential risks.
Enhancing your leadership style, you motivate, develop and inspire others to deliver quality. You are responsible for coaching, leveraging team member’s unique strengths, and managing performance to deliver on client expectations. With your growing knowledge of how business works, you play an important role in identifying opportunities that contribute to the success of our Firm. You are expected to lead with integrity and authenticity, articulating our purpose and values in a meaningful way. You embrace technology and innovation to enhance your delivery and encourage others to do the same.
Skills
Examples of the skills, knowledge, and experiences you need to lead and deliver value at this level include but are not limited to:
- Analyse and identify the linkages and interactions between the component parts of an entire system.
- Take ownership of projects, ensuring their successful planning, budgeting, execution, and completion.
- Partner with team leadership to ensure collective ownership of quality, timelines, and deliverables.
- Develop skills outside your comfort zone, and encourage others to do the same.
- Effectively mentor others.
- Use the review of work as an opportunity to deepen the expertise of team members.
- Address conflicts or issues, engaging in difficult conversations with clients, team members and other stakeholders, escalating where appropriate.
- Uphold and reinforce professional and technical standards (e.g. refer to specific PwC tax and audit guidance), the Firm's code of conduct, and independence requirements.
Key Responsibilities
Data Engineering Leadership:
- Lead the design, development, and optimization of complex data pipelines and workflows, ensuring robust data integration and transformation.
- Oversee the implementation of scalable data models and warehousing solutions to support large-scale data storage and analytics needs.
- Provide hands-on technical leadership and mentorship to a team of data engineers, guiding them on best practices for coding, testing, and deployment.
Cloud Solutions & Data Warehousing
- Architect and deploy cloud-based data solutions using platforms such as AWS, Azure, or Google Cloud.
- Leverage cloud-native services and serverless computing (e.g., AWS Lambda, Azure Functions) to build cost-efficient, high-performance data processing systems.
- Design and manage data warehousing solutions using cloud technologies, ensuring data is organized and easily accessible for analytical purposes.
PySpark & Big Data Processing
- Utilize PySpark to process large datasets and implement efficient distributed data processing solutions.
- Optimize data pipelines to handle both batch and streaming data for real-time insights.
Machine Learning & AI Integration
- Collaborate with data scientists and machine learning teams to integrate predictive models into the data ecosystem.
- Apply machine learning and AI techniques to automate and enhance data processing and analytics capabilities.
Data Visualization & Collaboration
- Provide structured, clean data to business users for use in reporting and dashboards through tools like Power BI, Tableau, and QuickSight.
- Partner with analytics teams to deliver actionable insights and drive data-driven decision-making across the organization.
Strategic Oversight & Stakeholder Engagement
- Align data engineering initiatives with broader business goals, ensuring that data solutions meet evolving organizational needs.
- Collaborate with senior leadership and cross-functional teams to define data strategies, roadmaps, and technical standards.
- Continuously evaluate new data technologies, methodologies, and best practices to drive innovation and improvement in data processes.
Required Skills And Experience
- 8+ years of experience in data engineering, with strong proficiency in Python, SQL, and data modeling.
- Expertise in cloud platforms (AWS, Azure, Google Cloud), including serverless computing and cloud-native services.
- Strong knowledge of data warehousing, big data solutions, and distributed computing using PySpark.
- Hands-on experience with machine learning and AI tools, with the ability to integrate predictive models into data pipelines.
- Basic familiarity with data visualization tools like Power BI, Tableau, or QuickSight.
- Excellent leadership, problem-solving, and communication skills, with a proven track record of managing and mentoring technical teams.
Qualifications
- Bachelor’s or Master’s degree in Computer Science, Data Science, Information Technology, or related fields.
- Proven experience in managing complex data projects and delivering high-quality, scalable data solutions.
- Strong ability to collaborate with stakeholders and drive strategic data initiatives.
- Min years of Experience Requirement: 8 years
- Credentials – Not Required
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